{
 "cells": [
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-04-17T06:13:46.531016Z",
     "start_time": "2024-04-17T06:13:46.517763Z"
    }
   },
   "cell_type": "code",
   "source": [
    "import pandas as pd\n",
    "\n",
    "# 获取数据 csv 数据\n",
    "data = pd.read_csv('data/zgpa_train.csv')\n",
    "data.head()"
   ],
   "id": "16b182c89c2d0f07",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "         date   open   high    low  close    volume\n",
       "0  2016-01-04  30.57  30.57  28.63  28.78  70997200\n",
       "1  2016-01-05  28.41  29.54  28.23  29.23  87498504\n",
       "2  2016-01-06  29.03  29.39  28.73  29.26  48012112\n",
       "3  2016-01-07  28.73  29.25  27.73  28.50  23647604\n",
       "4  2016-01-08  28.73  29.18  27.63  28.67  98239664"
      ],
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>open</th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "      <th>close</th>\n",
       "      <th>volume</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2016-01-04</td>\n",
       "      <td>30.57</td>\n",
       "      <td>30.57</td>\n",
       "      <td>28.63</td>\n",
       "      <td>28.78</td>\n",
       "      <td>70997200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2016-01-05</td>\n",
       "      <td>28.41</td>\n",
       "      <td>29.54</td>\n",
       "      <td>28.23</td>\n",
       "      <td>29.23</td>\n",
       "      <td>87498504</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2016-01-06</td>\n",
       "      <td>29.03</td>\n",
       "      <td>29.39</td>\n",
       "      <td>28.73</td>\n",
       "      <td>29.26</td>\n",
       "      <td>48012112</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2016-01-07</td>\n",
       "      <td>28.73</td>\n",
       "      <td>29.25</td>\n",
       "      <td>27.73</td>\n",
       "      <td>28.50</td>\n",
       "      <td>23647604</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2016-01-08</td>\n",
       "      <td>28.73</td>\n",
       "      <td>29.18</td>\n",
       "      <td>27.63</td>\n",
       "      <td>28.67</td>\n",
       "      <td>98239664</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 21
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-04-17T06:13:46.588397Z",
     "start_time": "2024-04-17T06:13:46.582191Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# 获取列名为 close 的数据，也就是收盘价\n",
    "price = data.loc[:, 'close']\n",
    "price.head()"
   ],
   "id": "d1b140a99b709131",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    28.78\n",
       "1    29.23\n",
       "2    29.26\n",
       "3    28.50\n",
       "4    28.67\n",
       "Name: close, dtype: float64"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 22
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-04-17T06:13:46.726351Z",
     "start_time": "2024-04-17T06:13:46.590395Z"
    }
   },
   "cell_type": "code",
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# 绘图\n",
    "fig1 = plt.figure(figsize=(8, 3), dpi=150, facecolor='white')\n",
    "plt.plot(price, color='blue')\n",
    "plt.title('Train Predict vs Actual')  # 标题\n",
    "plt.xlabel('Time Period')  # x轴标题\n",
    "plt.ylabel('Price')  # y轴标题\n",
    "plt.show()"
   ],
   "id": "ee98a0aa0c0013d7",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 1200x450 with 1 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 23
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-04-17T06:13:46.733169Z",
     "start_time": "2024-04-17T06:13:46.727456Z"
    }
   },
   "cell_type": "code",
   "source": [
    "#  数据归一化，将数据按比例缩放，使之落入一个小的特定区间 [0, 1] 之间\n",
    "# price_norm = price / max(price)\n",
    "price_norm = price / price.max()\n",
    "# 简单的归一化方法：最小-最大归一化（Min-Max Normalization）。\n",
    "# 将每个数据点除以数据集中的最大值。\n",
    "\n",
    "print(price_norm)"
   ],
   "id": "b2cfc305035654dc",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0      0.383273\n",
      "1      0.389266\n",
      "2      0.389666\n",
      "3      0.379545\n",
      "4      0.381808\n",
      "         ...   \n",
      "726    0.751099\n",
      "727    0.750566\n",
      "728    0.738447\n",
      "729    0.733120\n",
      "730    0.722466\n",
      "Name: close, Length: 731, dtype: float64\n"
     ]
    }
   ],
   "execution_count": 24
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": [
    "归一化后的数据有助于提高神经网络训练过程中的数值稳定性，因为所有的输入特征都在相同的数值范围内。这有助于避免某些特征在计算过程中对其他特征产生数值上的压制，从而使得网络权重的更新更加平衡，加速模型的收敛速度。\n",
    "\n",
    "归一化可以防止数值问题，如梯度消失或梯度爆炸，这些问题都可能在训练过程中出现，尤其是在RNN这类深度学习模型中。通过归一化，可以确保梯度在反向传播过程中保持稳定，从而提高训练效率。\n",
    "\n",
    "归一化后的数据有助于提高模型的泛化能力。因为所有的特征都在相同的尺度上，模型可以更好地学习特征之间的关系，而不是被某些尺度较大的特征所主导。这有助于模型在面对新的、未见过的数据时，依然能够做出准确的预测。\n",
    "\n",
    "在进行矩阵运算时，如果数据的数值过大，可能会导致数值溢出，影响计算结果的准确性。通过归一化，可以确保数据的数值在一个安全的范围内，避免这类问题的发生。\n",
    "\n",
    "在初始化模型参数时，通常会选择一些较小的数值以避免对训练过程产生不利影响。如果输入数据未经归一化，可能需要对参数初始化进行额外的调整，以适应不同尺度的输入数据。\n",
    "\n",
    "归一化有助于确保模型中各个特征的独立性，使得模型能够更好地识别和学习每个特征对预测结果的独立贡献。这对于提高模型的解释性和可理解性是非常有帮助的。"
   ],
   "id": "7bcfc94bc4fe8e4e"
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-04-17T06:13:46.755237Z",
     "start_time": "2024-04-17T06:13:46.734466Z"
    }
   },
   "cell_type": "code",
   "source": [
    "import numpy as np\n",
    "\n",
    "\n",
    "# 数据特征提取，x, y\n",
    "def extract_data(data, time_step):\n",
    "    \"\"\"\n",
    "    :param data 原始的时间序列数据，通常是一个包含数值的列表或数组\n",
    "    :param time_step 预测未来值的过去观测值的数量\n",
    "    \"\"\"\n",
    "    x = []  # 存储特征\n",
    "    y = []  # 标签\n",
    "    for i in range(len(data) - time_step):\n",
    "        x.append([a for a in data[i:i + time_step]])\n",
    "        y.append(data[i + time_step])  # 目标值\n",
    "\n",
    "    # 转换为 NumPy 数组，大多数机器学习模型期望输入数据是 NumPy 数组格式\n",
    "    x = np.array(x)\n",
    "    y = np.array(y)\n",
    "\n",
    "    # 将 x 数组重塑为三维数组，满足神经网络架构的输入要求\n",
    "    x = x.reshape(x.shape[0], x.shape[1], 1)\n",
    "    return x, y\n",
    "\n",
    "\n",
    "# 模型参数\n",
    "time_step = 8  # 使用过去 8 个时间步长的数据来预测下一个值\n",
    "x, y = extract_data(price_norm, time_step)  # 处理特征\n",
    "\n",
    "print(x, \"\\n\", x.shape)\n",
    "print(y, \"\\n\", y.shape)"
   ],
   "id": "465525844b389916",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[[0.38327341]\n",
      "  [0.38926621]\n",
      "  [0.38966573]\n",
      "  ...\n",
      "  [0.35637235]\n",
      "  [0.35876948]\n",
      "  [0.35583966]]\n",
      "\n",
      " [[0.38926621]\n",
      "  [0.38966573]\n",
      "  [0.37954455]\n",
      "  ...\n",
      "  [0.35876948]\n",
      "  [0.35583966]\n",
      "  [0.35583966]]\n",
      "\n",
      " [[0.38966573]\n",
      "  [0.37954455]\n",
      "  [0.3818085 ]\n",
      "  ...\n",
      "  [0.35583966]\n",
      "  [0.35583966]\n",
      "  [0.34531895]]\n",
      "\n",
      " ...\n",
      "\n",
      " [[0.80023971]\n",
      "  [0.79131709]\n",
      "  [0.7857238 ]\n",
      "  ...\n",
      "  [0.75229724]\n",
      "  [0.75109868]\n",
      "  [0.75056599]]\n",
      "\n",
      " [[0.79131709]\n",
      "  [0.7857238 ]\n",
      "  [0.77600213]\n",
      "  ...\n",
      "  [0.75109868]\n",
      "  [0.75056599]\n",
      "  [0.7384472 ]]\n",
      "\n",
      " [[0.7857238 ]\n",
      "  [0.77600213]\n",
      "  [0.7610867 ]\n",
      "  ...\n",
      "  [0.75056599]\n",
      "  [0.7384472 ]\n",
      "  [0.73312026]]] \n",
      " (723, 8, 1)\n",
      "[0.35583966 0.34531895 0.34358769 0.34944733 0.33639632 0.33133573\n",
      " 0.33226795 0.33280064 0.31908377 0.31895059 0.32001598 0.33173525\n",
      " 0.32307897 0.32893861 0.32454388 0.3254761  0.32481023 0.32174724\n",
      " 0.33093621 0.32987082 0.32813957 0.32827274 0.34225596 0.3359968\n",
      " 0.33479824 0.31548808 0.31735251 0.31615395 0.31948329 0.33173525\n",
      " 0.33439872 0.34998002 0.34332135 0.34665069 0.34771607 0.33919297\n",
      " 0.34318817 0.34412039 0.34744973 0.35770409 0.35597283 0.35757091\n",
      " 0.36702624 0.35730457 0.35943534 0.35330936 0.35677187 0.34878146\n",
      " 0.34598482 0.35583966 0.35344254 0.35490744 0.35597283 0.35251032\n",
      " 0.3475829  0.34478626 0.34944733 0.34998002 0.35477427 0.35623918\n",
      " 0.35583966 0.35317619 0.35504062 0.35876948 0.35890265 0.36156612\n",
      " 0.36023439 0.3611666  0.35943534 0.35850313 0.35650553 0.35477427\n",
      " 0.35504062 0.35530696 0.34798242 0.34478626 0.34518578 0.34598482\n",
      " 0.3521108  0.35224397 0.35077907 0.34944733 0.35344254 0.35370888\n",
      " 0.35357571 0.35370888 0.35117859 0.35077907 0.35104541 0.35064589\n",
      " 0.35477427 0.36556133 0.35983486 0.35876948 0.36050073 0.35743774\n",
      " 0.35783726 0.35797044 0.35330936 0.35570649 0.35570649 0.35557331\n",
      " 0.35570649 0.35757091 0.35677187 0.35703822 0.35597283 0.34998002\n",
      " 0.35171128 0.35397523 0.35810361 0.35650553 0.35836996 0.36183247\n",
      " 0.36156612 0.36050073 0.35996804 0.35770409 0.35783726 0.36609402\n",
      " 0.36675989 0.36715941 0.36849114 0.36982288 0.3656945  0.36476229\n",
      " 0.36795845 0.36462911 0.36462911 0.36755893 0.37115461 0.36662671\n",
      " 0.36556133 0.36436276 0.36436276 0.36303103 0.36263151 0.36303103\n",
      " 0.36422959 0.36782528 0.36782528 0.37115461 0.38034359 0.39472633\n",
      " 0.38620322 0.38686909 0.39299507 0.39419363 0.39339459 0.39845519\n",
      " 0.39166334 0.38846717 0.38846717 0.39073112 0.39006526 0.39019843\n",
      " 0.39219603 0.39872153 0.40005327 0.40511386 0.40604608 0.40831003\n",
      " 0.40937542 0.40484752 0.39952058 0.39392729 0.40284991 0.40125183\n",
      " 0.39885471 0.40338261 0.40178453 0.39499268 0.39472633 0.39179651\n",
      " 0.39246238 0.39206286 0.39792249 0.39499268 0.39392729 0.39153016\n",
      " 0.39139699 0.38913304 0.39259555 0.39153016 0.39179651 0.3928619\n",
      " 0.39645758 0.39725663 0.39552537 0.39246238 0.39699028 0.39805567\n",
      " 0.40404848 0.39805567 0.40284991 0.40231722 0.40311626 0.40324943\n",
      " 0.3993874  0.40245039 0.40311626 0.40258357 0.40271674 0.40351578\n",
      " 0.40324943 0.40338261 0.41523505 0.41816487 0.41630044 0.42216007\n",
      " 0.42695432 0.42469037 0.42682115 0.42096151 0.42216007 0.42016247\n",
      " 0.4200293  0.41883074 0.42269277 0.42322546 0.42801971 0.4285524\n",
      " 0.42522307 0.43028366 0.41123985 0.40964176 0.40857638 0.39872153\n",
      " 0.39978692 0.40018644 0.39965375 0.40484752 0.40671195 0.40711147\n",
      " 0.40498069 0.4089759  0.41230523 0.41190571 0.4129711  0.40910907\n",
      " 0.40884272 0.40631243 0.40631243 0.40604608 0.41070715 0.41909708\n",
      " 0.41630044 0.41936343 0.41723265 0.4200293  0.41923026 0.41896391\n",
      " 0.42229325 0.42335864 0.41150619 0.41576775 0.41736583 0.41683313\n",
      " 0.4180317  0.41923026 0.42069517 0.41789852 0.42082834 0.42056199\n",
      " 0.4200293  0.42748702 0.42682115 0.42748702 0.42349181 0.4240245\n",
      " 0.42082834 0.41962978 0.41869756 0.41470236 0.41456918 0.41430284\n",
      " 0.41603409 0.41416966 0.41363697 0.41123985 0.41616727 0.41576775\n",
      " 0.41603409 0.417499   0.41323745 0.41430284 0.42016247 0.41656679\n",
      " 0.42415768 0.42389133 0.42642163 0.42628845 0.42695432 0.4265548\n",
      " 0.43001731 0.43121587 0.42935144 0.4245572  0.4225596  0.41909708\n",
      " 0.41669996 0.41523505 0.41510188 0.41683313 0.41257158 0.41004128\n",
      " 0.41390332 0.41776535 0.41883074 0.42295912 0.43667599 0.44080437\n",
      " 0.4426688  0.44200293 0.44320149 0.44080437 0.43334665 0.44040485\n",
      " 0.44306832 0.46770542 0.46677321 0.48248768 0.48182181 0.48435211\n",
      " 0.47542948 0.47809296 0.48328672 0.50765748 0.5120522  0.51018777\n",
      " 0.54294846 0.53882008 0.53735517 0.54934079 0.53589027 0.53202823\n",
      " 0.55013983 0.54947396 0.57131442 0.57823945 0.58876015 0.58223465\n",
      " 0.56585431 0.55213744 0.55906246 0.58383273 0.57770675 0.59541883\n",
      " 0.5915568  0.59928086 0.59928086 0.60194433 0.5844986  0.60247703\n",
      " 0.59781595 0.58889333 0.57530963 0.60860301 0.62152084 0.61646025\n",
      " 0.61805833 0.62671461 0.61845785 0.63204155 0.64722333 0.66227194\n",
      " 0.65654548 0.6614729  0.66373685 0.63643628 0.64509256 0.64109735\n",
      " 0.63550406 0.64176322 0.63550406 0.63723532 0.67492343 0.66413637\n",
      " 0.64922093 0.63616993 0.64123052 0.64296178 0.63776801 0.62005593\n",
      " 0.60474098 0.61685977 0.62231988 0.62644826 0.62498335 0.65255027\n",
      " 0.65068584 0.6795845  0.68224797 0.67851911 0.68797443 0.69290185\n",
      " 0.70435477 0.6956985  0.6906379  0.69250233 0.68557731 0.70075909\n",
      " 0.68890665 0.68238114 0.68384605 0.67012918 0.68104941 0.68451192\n",
      " 0.6705287  0.66200559 0.66094021 0.66307098 0.65801039 0.66786523\n",
      " 0.67079505 0.67239313 0.67359169 0.66493541 0.66679984 0.67239313\n",
      " 0.68038354 0.68251432 0.68770808 0.71167932 0.7112798  0.71247836\n",
      " 0.72273272 0.73658277 0.74470635 0.73711546 0.74856838 0.75282994\n",
      " 0.75123186 0.76867759 0.80250366 0.80423492 0.80823012 0.80503396\n",
      " 0.80530031 0.80942869 0.80356905 0.82168065 0.81155946 0.83446531\n",
      " 0.88187508 0.88533759 0.88893328 0.87268611 0.92342522 0.95352244\n",
      " 0.95938207 0.99573845 0.98255427 0.94087095 0.94486616 0.93061659\n",
      " 0.91969636 0.91556799 0.8822746  0.85803702 0.87521641 0.89932082\n",
      " 0.87534958 0.86522839 0.90318285 0.92662139 0.89945399 0.91157278\n",
      " 0.89679052 0.88840059 0.89226262 0.93101611 0.93581036 0.94872819\n",
      " 0.93687575 0.93874018 0.93581036 0.88800107 0.89119723 0.88307364\n",
      " 0.91783194 0.89625782 0.89905447 0.89519244 0.88347317 0.90824344\n",
      " 0.90624584 0.91863098 0.94206952 0.98521774 0.98734852 0.98108936\n",
      " 0.98468504 0.9844187  0.99973365 1.         0.96910374 0.95378879\n",
      " 0.96457584 0.9280863  0.92835264 0.95099214 0.95631908 0.96337728\n",
      " 0.95938207 0.93354641 0.90171794 0.86988947 0.80916234 0.81622054\n",
      " 0.84365428 0.86855773 0.880277   0.8918631  0.89399387 0.8808097\n",
      " 0.85350912 0.86909042 0.85244373 0.8551072  0.87428419 0.86709282\n",
      " 0.89053136 0.89519244 0.89825543 0.87561593 0.86762552 0.88986549\n",
      " 0.89013184 0.93407911 0.93780796 0.93421228 0.92169397 0.8873352\n",
      " 0.86043415 0.85883606 0.82208017 0.83526435 0.82088161 0.81222533\n",
      " 0.81022773 0.81076042 0.82953789 0.86003463 0.85231056 0.83965908\n",
      " 0.83246771 0.80743108 0.79184978 0.79691037 0.80982821 0.80543348\n",
      " 0.81662006 0.8375283  0.82341191 0.79171661 0.76201891 0.76801172\n",
      " 0.76934345 0.75962179 0.76601412 0.79118391 0.78945266 0.79251565\n",
      " 0.79145026 0.80476761 0.80303636 0.78758823 0.77680117 0.79065122\n",
      " 0.79890798 0.7902517  0.77360501 0.76641364 0.77054202 0.78345985\n",
      " 0.78186177 0.75882275 0.77853243 0.77200693 0.79717672 0.81115994\n",
      " 0.80090558 0.80929551 0.79797576 0.80649887 0.81808496 0.81608736\n",
      " 0.82527634 0.82780663 0.79398056 0.79078439 0.7806632  0.78039686\n",
      " 0.76335065 0.7560261  0.73285391 0.72393128 0.74723665 0.70249034\n",
      " 0.71740578 0.70648555 0.70661872 0.72539619 0.75123186 0.74190971\n",
      " 0.72739379 0.74457318 0.74670396 0.73578373 0.73977893 0.73698229\n",
      " 0.74896791 0.77733387 0.78053003 0.78931948 0.79491277 0.77680117\n",
      " 0.77773339 0.78692236 0.78745505 0.76215208 0.7429751  0.73711546\n",
      " 0.73724863 0.76028765 0.74457318 0.77440405 0.77200693 0.76015448\n",
      " 0.75349581 0.72659475 0.72592889 0.72060194 0.73698229 0.75842323\n",
      " 0.77853243 0.79451325 0.79904115 0.8123585  0.80476761 0.80623252\n",
      " 0.80823012 0.805833   0.80250366 0.83007058 0.80343588 0.79198295\n",
      " 0.81142629 0.80143827 0.7967772  0.78239446 0.80636569 0.81835131\n",
      " 0.8078306  0.82927154 0.84298841 0.83832734 0.87415102 0.87015581\n",
      " 0.88041017 0.87028899 0.88760154 0.83806099 0.84099081 0.83632974\n",
      " 0.79904115 0.8259422  0.81701958 0.82767346 0.82980423 0.80703156\n",
      " 0.84924757 0.88001065 0.84778266 0.8576375  0.86309762 0.84818218\n",
      " 0.80103875 0.81169264 0.82274604 0.83166866 0.87228659 0.86203223\n",
      " 0.86496205 0.85590625 0.85830337 0.83593022 0.83646291 0.84112398\n",
      " 0.82700759 0.84232255 0.84432015 0.8505793  0.82900519 0.82780663\n",
      " 0.81622054 0.80956186 0.81701958 0.81582101 0.82461047 0.81502197\n",
      " 0.82088161 0.84085764 0.8395259  0.8310028  0.81435611 0.81435611\n",
      " 0.80343588 0.8033027  0.80716474 0.81701958 0.80023971 0.79131709\n",
      " 0.7857238  0.77600213 0.7610867  0.75229724 0.75109868 0.75056599\n",
      " 0.7384472  0.73312026 0.72246637] \n",
      " (723,)\n"
     ]
    }
   ],
   "execution_count": 25
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-04-17T06:13:46.797889Z",
     "start_time": "2024-04-17T06:13:46.757248Z"
    }
   },
   "cell_type": "code",
   "source": [
    "from keras import Input\n",
    "from keras.models import Sequential  #导入 Keras 中用于线性堆叠层的模型类\n",
    "from keras.layers import Dense, RNN, SimpleRNN, LSTM\n",
    "\n",
    "# 创建了一个Sequential模型实例，并将其赋值给变量model。实例是空的，没有添加任何层，但提供了一个框架，可以在此基础上构建神经网络模型\n",
    "model = Sequential()\n",
    "\n",
    "# 定义输入层\n",
    "# shape 参数定义了输入数据的维度\n",
    "# time_step 是时间步长，表示每个输入样本将包含 time_step 个时间点的数据，每个时间点的数据是一个特征（因此是1）\n",
    "model.add(Input(shape=(time_step, 1)))\n",
    "\n",
    "# 添加 RNN 层，使用的是 LSTM\n",
    "# units 参数指定了 RNN 层中的单元（神经元）数量，这里是5个\n",
    "# activation 参数定义了激活函数，这里是 ReLU（修正线性单元）\n",
    "model.add(SimpleRNN(units=5, activation='relu'))\n",
    "\n",
    "# 添加全连接的输出层\n",
    "# units 参数设置为 1，表示输出层只有一个神经元\n",
    "# activation 参数设置为 linear，不使用激活函数\n",
    "model.add(Dense(units=1, activation='linear'))\n",
    "\n",
    "# 编译\n",
    "# 指定优化器为 adam，这是一种常用的随机梯度下降优化器\n",
    "# 损失函数设置为 mean_squared_error，回归问题常用的损失函数，用于计算预测值和实际值之间的均方误差\n",
    "model.compile(optimizer='adam', loss='mean_squared_error')\n",
    "model.summary()"
   ],
   "id": "a4e98c17aa59d2a4",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\u001B[1mModel: \"sequential_3\"\u001B[0m\n"
      ],
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Model: \"sequential_3\"</span>\n",
       "</pre>\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
       "┃\u001B[1m \u001B[0m\u001B[1mLayer (type)                   \u001B[0m\u001B[1m \u001B[0m┃\u001B[1m \u001B[0m\u001B[1mOutput Shape          \u001B[0m\u001B[1m \u001B[0m┃\u001B[1m \u001B[0m\u001B[1m      Param #\u001B[0m\u001B[1m \u001B[0m┃\n",
       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
       "│ simple_rnn_3 (\u001B[38;5;33mSimpleRNN\u001B[0m)        │ (\u001B[38;5;45mNone\u001B[0m, \u001B[38;5;34m5\u001B[0m)              │            \u001B[38;5;34m35\u001B[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dense_3 (\u001B[38;5;33mDense\u001B[0m)                 │ (\u001B[38;5;45mNone\u001B[0m, \u001B[38;5;34m1\u001B[0m)              │             \u001B[38;5;34m6\u001B[0m │\n",
       "└─────────────────────────────────┴────────────────────────┴───────────────┘\n"
      ],
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
       "┃<span style=\"font-weight: bold\"> Layer (type)                    </span>┃<span style=\"font-weight: bold\"> Output Shape           </span>┃<span style=\"font-weight: bold\">       Param # </span>┃\n",
       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
       "│ simple_rnn_3 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">SimpleRNN</span>)        │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">5</span>)              │            <span style=\"color: #00af00; text-decoration-color: #00af00\">35</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dense_3 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                 │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>)              │             <span style=\"color: #00af00; text-decoration-color: #00af00\">6</span> │\n",
       "└─────────────────────────────────┴────────────────────────┴───────────────┘\n",
       "</pre>\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "\u001B[1m Total params: \u001B[0m\u001B[38;5;34m41\u001B[0m (164.00 B)\n"
      ],
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">41</span> (164.00 B)\n",
       "</pre>\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "\u001B[1m Trainable params: \u001B[0m\u001B[38;5;34m41\u001B[0m (164.00 B)\n"
      ],
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">41</span> (164.00 B)\n",
       "</pre>\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "\u001B[1m Non-trainable params: \u001B[0m\u001B[38;5;34m0\u001B[0m (0.00 B)\n"
      ],
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Non-trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (0.00 B)\n",
       "</pre>\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 26
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-04-17T06:14:00.035423Z",
     "start_time": "2024-04-17T06:13:46.798925Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# 训练模型\n",
    "# epochs 是训练的轮数\n",
    "# batch_size 是每次梯度下降中使用的数据样本数量，这里是 30。\n",
    "model.fit(x, y, epochs=200, batch_size=30)\n",
    "\n",
    "# 进行预测\n",
    "# 使用训练好的模型对训练集 x 进行预测，并将预测结果乘以 price 列表中的最大值，将预测结果缩放到与原始数据相同的尺度\n",
    "y_train_predict = model.predict(x) * max(price)\n",
    "\n",
    "# 将实际的标签 y 中的每个值乘以 price 列表中的最大值，这同样是为了将标签值缩放到与原始数据相同的尺度\n",
    "y_train = [i * max(price) for i in y]"
   ],
   "id": "840520090aa5df4b",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/200\n",
      "\u001B[1m25/25\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m1s\u001B[0m 2ms/step - loss: 0.4672\n",
      "Epoch 2/200\n",
      "\u001B[1m25/25\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 2ms/step - loss: 0.3143 \n",
      "Epoch 3/200\n",
      "\u001B[1m25/25\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 2ms/step - loss: 0.1239 \n",
      "Epoch 4/200\n",
      "\u001B[1m25/25\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 2ms/step - loss: 0.0358 \n",
      "Epoch 5/200\n",
      "\u001B[1m25/25\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 2ms/step - loss: 0.0202 \n",
      "Epoch 6/200\n",
      "\u001B[1m25/25\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 1ms/step - loss: 0.0181 \n",
      "Epoch 7/200\n",
      "\u001B[1m25/25\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 2ms/step - loss: 0.0165 \n",
      "Epoch 8/200\n",
      "\u001B[1m25/25\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 1ms/step - loss: 0.0157 \n",
      "Epoch 9/200\n",
      "\u001B[1m25/25\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 2ms/step - loss: 0.0148 \n",
      "Epoch 10/200\n",
      "\u001B[1m25/25\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 2ms/step - loss: 0.0128 \n",
      "Epoch 11/200\n",
      "\u001B[1m25/25\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 2ms/step - loss: 0.0120 \n",
      "Epoch 12/200\n",
      "\u001B[1m25/25\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 2ms/step - loss: 0.0111 \n",
      "Epoch 13/200\n",
      "\u001B[1m25/25\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 2ms/step - loss: 0.0097 \n",
      "Epoch 14/200\n",
      "\u001B[1m25/25\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 1ms/step - loss: 0.0090 \n",
      "Epoch 15/200\n",
      "\u001B[1m25/25\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 2ms/step - loss: 0.0074 \n",
      "Epoch 16/200\n",
      "\u001B[1m25/25\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 1ms/step - loss: 0.0065 \n",
      "Epoch 17/200\n",
      "\u001B[1m25/25\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 1ms/step - loss: 0.0057 \n",
      "Epoch 18/200\n",
      "\u001B[1m25/25\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 2ms/step - loss: 0.0048 \n",
      "Epoch 19/200\n",
      "\u001B[1m25/25\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 2ms/step - loss: 0.0040 \n",
      "Epoch 20/200\n",
      "\u001B[1m25/25\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 2ms/step - loss: 0.0034 \n",
      "Epoch 21/200\n",
      "\u001B[1m25/25\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 2ms/step - loss: 0.0027 \n",
      "Epoch 22/200\n",
      "\u001B[1m25/25\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 2ms/step - loss: 0.0021 \n",
      "Epoch 23/200\n",
      "\u001B[1m25/25\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 1ms/step - loss: 0.0017 \n",
      "Epoch 24/200\n",
      "\u001B[1m25/25\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 1ms/step - loss: 0.0013     \n",
      "Epoch 25/200\n",
      "\u001B[1m25/25\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 1ms/step - loss: 0.0013 \n",
      "Epoch 26/200\n",
      "\u001B[1m25/25\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 1ms/step - loss: 0.0011 \n",
      "Epoch 27/200\n",
      "\u001B[1m25/25\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 1ms/step - loss: 8.4784e-04 \n",
      "Epoch 28/200\n",
      "\u001B[1m25/25\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 1ms/step - loss: 7.3106e-04 \n",
      "Epoch 29/200\n",
      "\u001B[1m25/25\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 2ms/step - loss: 8.0509e-04\n",
      "Epoch 30/200\n",
      "\u001B[1m25/25\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 1ms/step - loss: 6.3757e-04 \n",
      "Epoch 31/200\n",
      "\u001B[1m25/25\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 1ms/step - loss: 6.4782e-04 \n",
      "Epoch 32/200\n",
      "\u001B[1m25/25\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 1ms/step - loss: 6.3835e-04 \n",
      "Epoch 33/200\n",
      "\u001B[1m25/25\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 1ms/step - loss: 7.2751e-04 \n",
      "Epoch 34/200\n",
      "\u001B[1m25/25\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 1ms/step - loss: 5.8664e-04 \n",
      "Epoch 35/200\n",
      "\u001B[1m25/25\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 1ms/step - loss: 7.1918e-04\n",
      "Epoch 36/200\n",
      "\u001B[1m25/25\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 1ms/step - loss: 6.4615e-04 \n",
      "Epoch 37/200\n",
      "\u001B[1m25/25\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 1ms/step - loss: 7.0733e-04\n",
      "Epoch 38/200\n",
      "\u001B[1m25/25\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 1ms/step - loss: 6.8003e-04\n",
      "Epoch 39/200\n",
      "\u001B[1m25/25\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 1ms/step - loss: 6.6321e-04 \n",
      "Epoch 40/200\n",
      "\u001B[1m25/25\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 1ms/step - loss: 6.7108e-04 \n",
      "Epoch 41/200\n",
      "\u001B[1m25/25\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 1ms/step - loss: 6.7698e-04 \n",
      "Epoch 42/200\n",
      "\u001B[1m25/25\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 1ms/step - loss: 6.6051e-04 \n",
      "Epoch 43/200\n",
      "\u001B[1m25/25\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 1ms/step - loss: 6.5484e-04 \n",
      "Epoch 44/200\n",
      "\u001B[1m25/25\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 1ms/step - loss: 5.5972e-04 \n",
      "Epoch 45/200\n",
      "\u001B[1m25/25\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 1ms/step - loss: 6.6069e-04 \n",
      "Epoch 46/200\n",
      "\u001B[1m25/25\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 1ms/step - loss: 5.9425e-04 \n",
      "Epoch 47/200\n",
      "\u001B[1m25/25\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 1ms/step - loss: 6.1848e-04 \n",
      "Epoch 48/200\n",
      "\u001B[1m25/25\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 1ms/step - loss: 6.3571e-04 \n",
      "Epoch 49/200\n",
      "\u001B[1m25/25\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 1ms/step - loss: 6.1589e-04 \n",
      "Epoch 50/200\n",
      "\u001B[1m25/25\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 1ms/step - loss: 6.0523e-04 \n",
      "Epoch 51/200\n",
      "\u001B[1m25/25\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 1ms/step - loss: 6.4059e-04 \n",
      "Epoch 52/200\n",
      "\u001B[1m25/25\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 1ms/step - loss: 6.0153e-04 \n",
      "Epoch 53/200\n",
      "\u001B[1m25/25\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 1ms/step - loss: 5.6439e-04 \n",
      "Epoch 54/200\n",
      "\u001B[1m25/25\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 1ms/step - loss: 7.4434e-04\n",
      "Epoch 55/200\n",
      "\u001B[1m25/25\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 1ms/step - loss: 6.3580e-04 \n",
      "Epoch 56/200\n",
      "\u001B[1m25/25\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 1ms/step - loss: 5.8407e-04 \n",
      "Epoch 57/200\n",
      "\u001B[1m25/25\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 1ms/step - loss: 6.1490e-04 \n",
      "Epoch 58/200\n",
      "\u001B[1m25/25\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 1ms/step - loss: 5.7881e-04 \n",
      "Epoch 59/200\n",
      "\u001B[1m25/25\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 2ms/step - loss: 6.4965e-04 \n",
      "Epoch 60/200\n",
      "\u001B[1m25/25\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 1ms/step - loss: 6.3484e-04 \n",
      "Epoch 61/200\n",
      "\u001B[1m25/25\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 1ms/step - loss: 6.5761e-04 \n",
      "Epoch 62/200\n",
      "\u001B[1m25/25\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 1ms/step - loss: 5.8188e-04 \n",
      "Epoch 63/200\n",
      "\u001B[1m25/25\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 1ms/step - loss: 5.5884e-04 \n",
      "Epoch 64/200\n",
      "\u001B[1m25/25\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 1ms/step - loss: 5.9843e-04 \n",
      "Epoch 65/200\n",
      "\u001B[1m25/25\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 1ms/step - loss: 6.1688e-04 \n",
      "Epoch 66/200\n",
      "\u001B[1m25/25\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 2ms/step - loss: 6.2601e-04 \n",
      "Epoch 67/200\n",
      "\u001B[1m25/25\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 1ms/step - loss: 6.5792e-04 \n",
      "Epoch 68/200\n",
      "\u001B[1m25/25\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 1ms/step - loss: 6.3792e-04 \n",
      "Epoch 69/200\n",
      "\u001B[1m25/25\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 1ms/step - loss: 5.6706e-04 \n",
      "Epoch 70/200\n",
      "\u001B[1m25/25\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 1ms/step - loss: 5.7185e-04 \n",
      "Epoch 71/200\n",
      "\u001B[1m25/25\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 2ms/step - loss: 6.5777e-04 \n",
      "Epoch 72/200\n",
      "\u001B[1m25/25\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 1ms/step - loss: 5.6196e-04 \n",
      "Epoch 73/200\n",
      "\u001B[1m25/25\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 1ms/step - loss: 5.5353e-04 \n",
      "Epoch 74/200\n",
      "\u001B[1m25/25\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 1ms/step - loss: 5.9680e-04 \n",
      "Epoch 75/200\n",
      "\u001B[1m25/25\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 1ms/step - loss: 7.7406e-04 \n",
      "Epoch 76/200\n",
      "\u001B[1m25/25\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 1ms/step - loss: 6.7857e-04\n",
      "Epoch 77/200\n",
      "\u001B[1m25/25\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 1ms/step - loss: 6.2953e-04 \n",
      "Epoch 78/200\n",
      "\u001B[1m25/25\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 1ms/step - loss: 6.3993e-04 \n",
      "Epoch 79/200\n",
      "\u001B[1m25/25\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 1ms/step - loss: 6.5653e-04 \n",
      "Epoch 80/200\n",
      "\u001B[1m25/25\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 1ms/step - loss: 6.7462e-04 \n",
      "Epoch 81/200\n",
      "\u001B[1m25/25\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 1ms/step - loss: 6.9421e-04 \n",
      "Epoch 82/200\n",
      "\u001B[1m25/25\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 1ms/step - loss: 6.1942e-04 \n",
      "Epoch 83/200\n",
      "\u001B[1m25/25\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 1ms/step - loss: 7.5539e-04 \n",
      "Epoch 84/200\n",
      "\u001B[1m25/25\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 2ms/step - loss: 5.2593e-04 \n",
      "Epoch 85/200\n",
      "\u001B[1m25/25\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 1ms/step - loss: 6.1752e-04 \n",
      "Epoch 86/200\n",
      "\u001B[1m25/25\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 1ms/step - loss: 5.9733e-04 \n",
      "Epoch 87/200\n",
      "\u001B[1m25/25\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 1ms/step - loss: 6.1867e-04 \n",
      "Epoch 88/200\n",
      "\u001B[1m25/25\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 2ms/step - loss: 6.2178e-04 \n",
      "Epoch 89/200\n",
      "\u001B[1m25/25\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 1ms/step - loss: 5.5011e-04 \n",
      "Epoch 90/200\n",
      "\u001B[1m25/25\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 1ms/step - loss: 6.2677e-04\n",
      "Epoch 91/200\n",
      "\u001B[1m25/25\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 1ms/step - loss: 5.7907e-04 \n",
      "Epoch 92/200\n",
      "\u001B[1m25/25\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 1ms/step - loss: 6.4696e-04 \n",
      "Epoch 93/200\n",
      "\u001B[1m25/25\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 1ms/step - loss: 5.8080e-04 \n",
      "Epoch 94/200\n",
      "\u001B[1m25/25\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 1ms/step - loss: 5.8787e-04 \n",
      "Epoch 95/200\n",
      "\u001B[1m25/25\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 1ms/step - loss: 6.4212e-04 \n",
      "Epoch 96/200\n",
      "\u001B[1m25/25\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 1ms/step - loss: 5.7526e-04 \n",
      "Epoch 97/200\n",
      "\u001B[1m25/25\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 1ms/step - loss: 6.2643e-04 \n",
      "Epoch 98/200\n",
      "\u001B[1m25/25\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 1ms/step - loss: 6.5448e-04 \n",
      "Epoch 99/200\n",
      "\u001B[1m25/25\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 1ms/step - loss: 6.0116e-04 \n",
      "Epoch 100/200\n",
      "\u001B[1m25/25\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 1ms/step - loss: 5.8387e-04 \n",
      "Epoch 101/200\n",
      "\u001B[1m25/25\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 1ms/step - loss: 6.0371e-04 \n",
      "Epoch 102/200\n",
      "\u001B[1m25/25\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 1ms/step - loss: 6.4596e-04 \n",
      "Epoch 103/200\n",
      "\u001B[1m25/25\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 1ms/step - loss: 6.5441e-04 \n",
      "Epoch 104/200\n",
      "\u001B[1m25/25\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 1ms/step - loss: 6.4591e-04 \n",
      "Epoch 105/200\n",
      "\u001B[1m25/25\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 1ms/step - loss: 6.1255e-04 \n",
      "Epoch 106/200\n",
      "\u001B[1m25/25\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 1ms/step - loss: 6.0317e-04 \n",
      "Epoch 107/200\n",
      "\u001B[1m25/25\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 1ms/step - loss: 5.8733e-04 \n",
      "Epoch 108/200\n",
      "\u001B[1m25/25\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 1ms/step - loss: 7.2735e-04 \n",
      "Epoch 109/200\n",
      "\u001B[1m25/25\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 1ms/step - loss: 6.5475e-04 \n",
      "Epoch 110/200\n",
      "\u001B[1m25/25\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 1ms/step - loss: 5.4710e-04 \n",
      "Epoch 111/200\n",
      "\u001B[1m25/25\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 1ms/step - loss: 6.1207e-04 \n",
      "Epoch 112/200\n",
      "\u001B[1m25/25\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 1ms/step - loss: 5.6865e-04 \n",
      "Epoch 113/200\n",
      "\u001B[1m25/25\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 1ms/step - loss: 5.6765e-04 \n",
      "Epoch 114/200\n",
      "\u001B[1m25/25\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 1ms/step - loss: 6.2409e-04 \n",
      "Epoch 115/200\n",
      "\u001B[1m25/25\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 1ms/step - loss: 5.8999e-04 \n",
      "Epoch 116/200\n",
      "\u001B[1m25/25\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 1ms/step - loss: 6.1918e-04 \n",
      "Epoch 117/200\n",
      "\u001B[1m25/25\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 1ms/step - loss: 6.1142e-04 \n",
      "Epoch 118/200\n",
      "\u001B[1m25/25\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 1ms/step - loss: 5.2973e-04 \n",
      "Epoch 119/200\n",
      "\u001B[1m25/25\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 1ms/step - loss: 6.0212e-04 \n",
      "Epoch 120/200\n",
      "\u001B[1m25/25\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 1ms/step - loss: 5.8963e-04 \n",
      "Epoch 121/200\n",
      "\u001B[1m25/25\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 1ms/step - loss: 5.7031e-04 \n",
      "Epoch 122/200\n",
      "\u001B[1m25/25\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 1ms/step - loss: 6.7391e-04 \n",
      "Epoch 123/200\n",
      "\u001B[1m25/25\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 2ms/step - loss: 6.0142e-04 \n",
      "Epoch 124/200\n",
      "\u001B[1m25/25\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 2ms/step - loss: 6.6042e-04 \n",
      "Epoch 125/200\n",
      "\u001B[1m25/25\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 2ms/step - loss: 6.1860e-04 \n",
      "Epoch 126/200\n",
      "\u001B[1m25/25\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 1ms/step - loss: 6.1173e-04 \n",
      "Epoch 127/200\n",
      "\u001B[1m25/25\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 1ms/step - loss: 5.8236e-04 \n",
      "Epoch 128/200\n",
      "\u001B[1m25/25\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 1ms/step - loss: 6.0008e-04 \n",
      "Epoch 129/200\n",
      "\u001B[1m25/25\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 2ms/step - loss: 5.5674e-04 \n",
      "Epoch 130/200\n",
      "\u001B[1m25/25\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 2ms/step - loss: 5.8016e-04 \n",
      "Epoch 131/200\n",
      "\u001B[1m25/25\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 1ms/step - loss: 5.9537e-04 \n",
      "Epoch 132/200\n",
      "\u001B[1m25/25\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 1ms/step - loss: 6.5946e-04 \n",
      "Epoch 133/200\n",
      "\u001B[1m25/25\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 1ms/step - loss: 6.2038e-04\n",
      "Epoch 134/200\n",
      "\u001B[1m25/25\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 1ms/step - loss: 5.8042e-04 \n",
      "Epoch 135/200\n",
      "\u001B[1m25/25\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 1ms/step - loss: 5.7803e-04 \n",
      "Epoch 136/200\n",
      "\u001B[1m25/25\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 1ms/step - loss: 5.4562e-04 \n",
      "Epoch 137/200\n",
      "\u001B[1m25/25\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 1ms/step - loss: 6.0456e-04 \n",
      "Epoch 138/200\n",
      "\u001B[1m25/25\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 1ms/step - loss: 5.7647e-04 \n",
      "Epoch 139/200\n",
      "\u001B[1m25/25\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 1ms/step - loss: 6.2496e-04 \n",
      "Epoch 140/200\n",
      "\u001B[1m25/25\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 1ms/step - loss: 5.9825e-04 \n",
      "Epoch 141/200\n",
      "\u001B[1m25/25\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 1ms/step - loss: 5.7814e-04 \n",
      "Epoch 142/200\n",
      "\u001B[1m25/25\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 1ms/step - loss: 5.6897e-04 \n",
      "Epoch 143/200\n",
      "\u001B[1m25/25\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 1ms/step - loss: 6.5397e-04 \n",
      "Epoch 144/200\n",
      "\u001B[1m25/25\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 1ms/step - loss: 5.9941e-04 \n",
      "Epoch 145/200\n",
      "\u001B[1m25/25\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 1ms/step - loss: 6.1836e-04 \n",
      "Epoch 146/200\n",
      "\u001B[1m25/25\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 1ms/step - loss: 5.7013e-04 \n",
      "Epoch 147/200\n",
      "\u001B[1m25/25\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 1ms/step - loss: 6.0285e-04 \n",
      "Epoch 148/200\n",
      "\u001B[1m25/25\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 1ms/step - loss: 6.0674e-04 \n",
      "Epoch 149/200\n",
      "\u001B[1m25/25\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 1ms/step - loss: 6.7305e-04 \n",
      "Epoch 150/200\n",
      "\u001B[1m25/25\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 1ms/step - loss: 6.5318e-04 \n",
      "Epoch 151/200\n",
      "\u001B[1m25/25\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 1ms/step - loss: 5.4146e-04 \n",
      "Epoch 152/200\n",
      "\u001B[1m25/25\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 1ms/step - loss: 6.0434e-04 \n",
      "Epoch 153/200\n",
      "\u001B[1m25/25\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 1ms/step - loss: 6.2853e-04 \n",
      "Epoch 154/200\n",
      "\u001B[1m25/25\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 1ms/step - loss: 5.8871e-04 \n",
      "Epoch 155/200\n",
      "\u001B[1m25/25\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 1ms/step - loss: 7.2293e-04 \n",
      "Epoch 156/200\n",
      "\u001B[1m25/25\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 1ms/step - loss: 5.9471e-04 \n",
      "Epoch 157/200\n",
      "\u001B[1m25/25\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 1ms/step - loss: 6.0982e-04 \n",
      "Epoch 158/200\n",
      "\u001B[1m25/25\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 1ms/step - loss: 5.9458e-04 \n",
      "Epoch 159/200\n",
      "\u001B[1m25/25\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 1ms/step - loss: 4.7952e-04 \n",
      "Epoch 160/200\n",
      "\u001B[1m25/25\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 1ms/step - loss: 5.5481e-04 \n",
      "Epoch 161/200\n",
      "\u001B[1m25/25\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 1ms/step - loss: 6.5283e-04 \n",
      "Epoch 162/200\n",
      "\u001B[1m25/25\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 1ms/step - loss: 7.2643e-04 \n",
      "Epoch 163/200\n",
      "\u001B[1m25/25\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 1ms/step - loss: 5.4253e-04 \n",
      "Epoch 164/200\n",
      "\u001B[1m25/25\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 1ms/step - loss: 5.9630e-04 \n",
      "Epoch 165/200\n",
      "\u001B[1m25/25\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 1ms/step - loss: 5.4685e-04 \n",
      "Epoch 166/200\n",
      "\u001B[1m25/25\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 1ms/step - loss: 5.7097e-04 \n",
      "Epoch 167/200\n",
      "\u001B[1m25/25\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 2ms/step - loss: 5.7539e-04 \n",
      "Epoch 168/200\n",
      "\u001B[1m25/25\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 1ms/step - loss: 5.1349e-04 \n",
      "Epoch 169/200\n",
      "\u001B[1m25/25\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 2ms/step - loss: 7.1093e-04 \n",
      "Epoch 170/200\n",
      "\u001B[1m25/25\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 1ms/step - loss: 5.3681e-04 \n",
      "Epoch 171/200\n",
      "\u001B[1m25/25\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 1ms/step - loss: 5.9378e-04\n",
      "Epoch 172/200\n",
      "\u001B[1m25/25\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 1ms/step - loss: 5.9315e-04 \n",
      "Epoch 173/200\n",
      "\u001B[1m25/25\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 1ms/step - loss: 5.8280e-04 \n",
      "Epoch 174/200\n",
      "\u001B[1m25/25\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 1ms/step - loss: 5.6391e-04 \n",
      "Epoch 175/200\n",
      "\u001B[1m25/25\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 1ms/step - loss: 5.1755e-04 \n",
      "Epoch 176/200\n",
      "\u001B[1m25/25\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 1ms/step - loss: 5.3379e-04 \n",
      "Epoch 177/200\n",
      "\u001B[1m25/25\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 1ms/step - loss: 5.5114e-04 \n",
      "Epoch 178/200\n",
      "\u001B[1m25/25\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 1ms/step - loss: 5.5089e-04 \n",
      "Epoch 179/200\n",
      "\u001B[1m25/25\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 1ms/step - loss: 5.3053e-04 \n",
      "Epoch 180/200\n",
      "\u001B[1m25/25\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 1ms/step - loss: 6.3901e-04 \n",
      "Epoch 181/200\n",
      "\u001B[1m25/25\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 1ms/step - loss: 6.4389e-04 \n",
      "Epoch 182/200\n",
      "\u001B[1m25/25\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 1ms/step - loss: 5.9366e-04 \n",
      "Epoch 183/200\n",
      "\u001B[1m25/25\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 1ms/step - loss: 5.9091e-04 \n",
      "Epoch 184/200\n",
      "\u001B[1m25/25\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 1ms/step - loss: 5.4439e-04 \n",
      "Epoch 185/200\n",
      "\u001B[1m25/25\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 1ms/step - loss: 6.3565e-04\n",
      "Epoch 186/200\n",
      "\u001B[1m25/25\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 1ms/step - loss: 6.4207e-04 \n",
      "Epoch 187/200\n",
      "\u001B[1m25/25\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 1ms/step - loss: 5.9423e-04 \n",
      "Epoch 188/200\n",
      "\u001B[1m25/25\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 1ms/step - loss: 5.6657e-04 \n",
      "Epoch 189/200\n",
      "\u001B[1m25/25\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 1ms/step - loss: 5.5875e-04 \n",
      "Epoch 190/200\n",
      "\u001B[1m25/25\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 1ms/step - loss: 5.9242e-04 \n",
      "Epoch 191/200\n",
      "\u001B[1m25/25\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 1ms/step - loss: 5.4312e-04 \n",
      "Epoch 192/200\n",
      "\u001B[1m25/25\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 1ms/step - loss: 4.8959e-04 \n",
      "Epoch 193/200\n",
      "\u001B[1m25/25\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 1ms/step - loss: 5.5560e-04 \n",
      "Epoch 194/200\n",
      "\u001B[1m25/25\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 1ms/step - loss: 5.2855e-04 \n",
      "Epoch 195/200\n",
      "\u001B[1m25/25\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 1ms/step - loss: 5.7430e-04 \n",
      "Epoch 196/200\n",
      "\u001B[1m25/25\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 1ms/step - loss: 5.4240e-04 \n",
      "Epoch 197/200\n",
      "\u001B[1m25/25\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 1ms/step - loss: 6.6607e-04 \n",
      "Epoch 198/200\n",
      "\u001B[1m25/25\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 1ms/step - loss: 4.9647e-04 \n",
      "Epoch 199/200\n",
      "\u001B[1m25/25\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 1ms/step - loss: 6.2134e-04 \n",
      "Epoch 200/200\n",
      "\u001B[1m25/25\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 1ms/step - loss: 5.5815e-04 \n",
      "\u001B[1m23/23\u001B[0m \u001B[32m━━━━━━━━━━━━━━━━━━━━\u001B[0m\u001B[37m\u001B[0m \u001B[1m0s\u001B[0m 6ms/step\n"
     ]
    }
   ],
   "execution_count": 27
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-04-17T06:14:00.174834Z",
     "start_time": "2024-04-17T06:14:00.036629Z"
    }
   },
   "cell_type": "code",
   "source": [
    "fig2 = plt.figure(figsize=(8, 3), dpi=150, facecolor='white')\n",
    "plt.plot(y_train, color='blue', label='Actual')\n",
    "plt.plot(y_train_predict, color='red', label='Predict')\n",
    "plt.title('Train Predict vs Actual')  # 标题\n",
    "plt.xlabel('Time Period')  # x轴标题\n",
    "plt.ylabel('Price')  # y轴标题\n",
    "plt.legend(loc='best')  # 添加图例到图形\n",
    "plt.show()"
   ],
   "id": "ea546fd1f6029a67",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 1200x450 with 1 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 28
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
